Plyr Package
David Asare Kumi
4/6/2020
plyr: Split-Apply-Combine
plyr is an R package that makes it simple to split data apart, do stuff to it, and mash it back together. This is a common data-manipulation step. Importantly, plyr makes it easy to control the input and output data format with a consistent syntax.
Or, from the documentation:
“plyr is a set of tools that solves a common set of problems: you need to break a big problem down into manageable pieces, operate on each piece and then put all the pieces back together. It’s already possible to do this with split and the apply functions, but plyr just makes it all a bit easier…”
This is a very quick introduction to plyr. For more details see Hadley Wickham’s introductory guide The split-apply-combine strategy for data analysis. There’s quite a bit of discussion online in general, and especially on stackoverflow.com.
Why use apply functions instead of for loops?
The code is cleaner (once you’re familiar with the concept). The code can be easier to code and read, and less error prone because you don’t have to deal with subsetting and you don’t have to deal with saving your results.
apply functions can be faster than for loops, sometimes dramatically.
plyr basics
plyr builds on the built-in apply functions by giving you control over the input and output formats and keeping the syntax consistent across all variations. It also adds some niceties like error processing, parallel processing, and progress bars.
The basic format is two letters followed by ply(). The first letter refers to the format in and the second to the format out.
The three main letters are:
d = data frame.
a = array (includes matrices).
l = list.
So, ddply means: take a data frame, split it up, do something to it, and return a data frame. I find I use this the majority of the time since I often work with data frames. ldply means: take a list, split it up, do something to it, and return a data frame. This extends to all combinations. In the following table, the columns are the input formats and the rows are the output format:
object type dataframe list array
dataframe ddply ldply adply
list dlply llply alply
array daply laply aaply
- I’ve ignored some less common format options:
m = multi-argument function input.
r = replicate a function n times.
_ = throw away the output.
- For plotting, you might find the underscore (_) option useful. It will do something with the data (say add line segments to a plot) and then throw away the output (e.g., d_ply()).
Base R apply functions and plyr
- plyr provides a consistent and easy-to-work-with format for apply functions with control over the input and output formats. Some of the functionality can be duplicated with base R functions (but with less consistent syntax). Also, few R apply functions work directly with data frames as input and output and data frames are a common object class to work with.
A general example with plyr
- Let’s take a simple example. We’ll take a data frame, split it up by year, calculate the coefficient of variation of the count, and return a data frame. This could easily be done on one line, but I’m expanding it here to show the format a more complex function could take.
set.seed(1)
d <- data.frame(year = rep(2000:2002, each = 3),
count = round(runif(9, 0, 20)))
print(d) year count
1 2000 5
2 2000 7
3 2000 11
4 2001 18
5 2001 4
6 2001 18
7 2002 19
8 2002 13
9 2002 13library(plyr)
ddply(d, "year", function(x) {
mean.count <- mean(x$count)
sd.count <- sd(x$count)
cv <- sd.count/mean.count
data.frame(cv.count = cv)
}) year cv.count
1 2000 0.3984848
2 2001 0.6062178
3 2002 0.2309401transform and summarise
- It is often convenient to use these functions within one of the **ply functions. transform acts as it would normally as the base R function and modifies an existing data frame. summarise creates a new condensed data frame.
ddply(d, "year", summarise, mean.count = mean(count)) year mean.count
1 2000 7.666667
2 2001 13.333333
3 2002 15.000000ddply(d, "year", transform, total.count = sum(count)) year count total.count
1 2000 5 23
2 2000 7 23
3 2000 11 23
4 2001 18 40
5 2001 4 40
6 2001 18 40
7 2002 19 45
8 2002 13 45
9 2002 13 45- Bonus function: mutate. mutate works like transform but lets you build on columns.
ddply(d, "year", mutate, mu = mean(count), sigma = sd(count),
cv = sigma/mu) year count mu sigma cv
1 2000 5 7.666667 3.055050 0.3984848
2 2000 7 7.666667 3.055050 0.3984848
3 2000 11 7.666667 3.055050 0.3984848
4 2001 18 13.333333 8.082904 0.6062178
5 2001 4 13.333333 8.082904 0.6062178
6 2001 18 13.333333 8.082904 0.6062178
7 2002 19 15.000000 3.464102 0.2309401
8 2002 13 15.000000 3.464102 0.2309401
9 2002 13 15.000000 3.464102 0.2309401Plotting with plyr
- You can use plyr to plot data by throwing away the output with an underscore (_). This is a bit cleaner than a for loop since you don’t have to subset the data manually.
par(mfrow = c(1, 3), mar = c(2, 2, 1, 1), oma = c(3, 3, 0, 0))
d_ply(d, "year", transform, plot(count, main = unique(year), type = "o"))
mtext("count", side = 1, outer = TRUE, line = 1)
mtext("frequency", side = 2, outer = TRUE, line = 1)
Nested chunking of the data
baseball.dat <- subset(baseball, year > 2000) # data from the plyr package
x <- ddply(baseball.dat, c("year", "team"), summarize,
homeruns = sum(hr))
head(x) year team homeruns
1 2001 ANA 4
2 2001 ARI 155
3 2001 ATL 63
4 2001 BAL 58
5 2001 BOS 77
6 2001 CHA 63Other useful options
Dealing with errors
- You can use the failwith function to control how errors are dealt with.
f <- function(x) if (x == 1) stop("Error!") else 1
safe.f <- failwith(NA, f, quiet = TRUE)
# llply(1:2, f)
llply(1:2, safe.f)[[1]]
[1] NA
[[2]]
[1] 1Parallel processing
- In conjunction with a package such as doParallel you can run your function separately on each core of your computer. On a dual core machine this make your code up to twice as fast. Simply register the cores and then set .parallel = TRUE. Look at the elapsed time in these examples:
x <- c(1:10)
wait <- function(i) Sys.sleep(0.1)
system.time(llply(x, wait)) user system elapsed
0 0 1 system.time(sapply(x, wait)) user system elapsed
0 0 1 So, why would I not want to use plyr?
- plyr can be slow — particularly if you are working with very large datasets that involve a lot of subsetting. Hadley is working on this and an in-development version of plyr, dplyr, can run much faster (https://github.com/hadley/dplyr). However, it’s important to remember that typically the speed that you can write code and understand it later is the rate-limiting step.
Some examples using the package plyr
library(plyr)
#Example dataset from ggplot
library(ggplot2)
data(mpg)
str(mpg)Classes 'tbl_df', 'tbl' and 'data.frame': 234 obs. of 11 variables:
$ manufacturer: chr "audi" "audi" "audi" "audi" ...
$ model : chr "a4" "a4" "a4" "a4" ...
$ displ : num 1.8 1.8 2 2 2.8 2.8 3.1 1.8 1.8 2 ...
$ year : int 1999 1999 2008 2008 1999 1999 2008 1999 1999 2008 ...
$ cyl : int 4 4 4 4 6 6 6 4 4 4 ...
$ trans : chr "auto(l5)" "manual(m5)" "manual(m6)" "auto(av)" ...
$ drv : chr "f" "f" "f" "f" ...
$ cty : int 18 21 20 21 16 18 18 18 16 20 ...
$ hwy : int 29 29 31 30 26 26 27 26 25 28 ...
$ fl : chr "p" "p" "p" "p" ...
$ class : chr "compact" "compact" "compact" "compact" ...#Simplify the dataset
data <- mpg[,c(1,7:9)]
str(data)Classes 'tbl_df', 'tbl' and 'data.frame': 234 obs. of 4 variables:
$ manufacturer: chr "audi" "audi" "audi" "audi" ...
$ drv : chr "f" "f" "f" "f" ...
$ cty : int 18 21 20 21 16 18 18 18 16 20 ...
$ hwy : int 29 29 31 30 26 26 27 26 25 28 ...# Summarising/ Aggregating Data
ddply(data, .(manufacturer), summarize, avgcty = mean(cty)) manufacturer avgcty
1 audi 17.61111
2 chevrolet 15.00000
3 dodge 13.13514
4 ford 14.00000
5 honda 24.44444
6 hyundai 18.64286
7 jeep 13.50000
8 land rover 11.50000
9 lincoln 11.33333
10 mercury 13.25000
11 nissan 18.07692
12 pontiac 17.00000
13 subaru 19.28571
14 toyota 18.52941
15 volkswagen 20.92593# you can perform multiple functions in a single call
ddply(data, .(manufacturer), summarize, avgcty = mean(cty), sdcty = sd(cty), maxhwy = max(hwy)) manufacturer avgcty sdcty maxhwy
1 audi 17.61111 1.9745108 31
2 chevrolet 15.00000 2.9249881 30
3 dodge 13.13514 2.4850907 24
4 ford 14.00000 1.9148542 26
5 honda 24.44444 1.9436506 36
6 hyundai 18.64286 1.4990840 31
7 jeep 13.50000 2.5071327 22
8 land rover 11.50000 0.5773503 18
9 lincoln 11.33333 0.5773503 18
10 mercury 13.25000 0.5000000 19
11 nissan 18.07692 3.4268921 32
12 pontiac 17.00000 1.0000000 28
13 subaru 19.28571 0.9138735 27
14 toyota 18.52941 4.0469614 37
15 volkswagen 20.92593 4.5567020 44# you can summarize data by a combination of variables/factors
ddply(data, .(manufacturer, drv), summarize, avgcty = mean(cty), sdcty = sd(cty), maxhwy = max(hwy)) manufacturer drv avgcty sdcty maxhwy
1 audi 4 16.81818 1.6624188 28
2 audi f 18.85714 1.8644545 31
3 chevrolet 4 12.50000 1.7320508 19
4 chevrolet f 18.80000 1.9235384 30
5 chevrolet r 14.10000 1.6633300 26
6 dodge 4 12.00000 1.7435596 19
7 dodge f 15.81818 1.8340219 24
8 ford 4 13.30769 0.9473309 19
9 ford r 14.75000 2.4167973 26
10 honda f 24.44444 1.9436506 36
11 hyundai f 18.64286 1.4990840 31
12 jeep 4 13.50000 2.5071327 22
13 land rover 4 11.50000 0.5773503 18
14 lincoln r 11.33333 0.5773503 18
15 mercury 4 13.25000 0.5000000 19
16 nissan 4 13.75000 1.2583057 20
17 nissan f 20.00000 1.8708287 32
18 pontiac f 17.00000 1.0000000 28
19 subaru 4 19.28571 0.9138735 27
20 toyota 4 14.93333 1.4375906 22
21 toyota f 21.36842 3.0223340 37
22 volkswagen f 20.92593 4.5567020 44# note the package reshape/reshape2 is an elegant alternative for aggregating many variables at one time
# note the differences between the commands "summarize" and "transform"
ddply(data, .(drv), summarize, avgcty = mean(cty)) drv avgcty
1 4 14.3301
2 f 19.9717
3 r 14.0800ddply(data, .(drv), transform, avgcty = mean(cty)) manufacturer drv cty hwy avgcty
1 audi 4 18 26 14.3301
2 audi 4 16 25 14.3301
3 audi 4 20 28 14.3301
4 audi 4 19 27 14.3301
5 audi 4 15 25 14.3301
6 audi 4 17 25 14.3301
7 audi 4 17 25 14.3301
8 audi 4 15 25 14.3301
9 audi 4 15 24 14.3301
10 audi 4 17 25 14.3301
11 audi 4 16 23 14.3301
12 chevrolet 4 14 19 14.3301
13 chevrolet 4 11 14 14.3301
14 chevrolet 4 11 15 14.3301
15 chevrolet 4 14 17 14.3301
16 dodge 4 15 19 14.3301
17 dodge 4 14 18 14.3301
18 dodge 4 13 17 14.3301
19 dodge 4 14 17 14.3301
20 dodge 4 14 19 14.3301
21 dodge 4 14 19 14.3301
22 dodge 4 9 12 14.3301
23 dodge 4 11 17 14.3301
24 dodge 4 11 15 14.3301
25 dodge 4 13 17 14.3301
26 dodge 4 13 17 14.3301
27 dodge 4 9 12 14.3301
28 dodge 4 13 17 14.3301
29 dodge 4 11 16 14.3301
30 dodge 4 13 18 14.3301
31 dodge 4 11 15 14.3301
32 dodge 4 12 16 14.3301
33 dodge 4 9 12 14.3301
34 dodge 4 13 17 14.3301
35 dodge 4 13 17 14.3301
36 dodge 4 12 16 14.3301
37 dodge 4 9 12 14.3301
38 dodge 4 11 15 14.3301
39 dodge 4 11 16 14.3301
40 dodge 4 13 17 14.3301
41 dodge 4 11 15 14.3301
42 ford 4 14 17 14.3301
43 ford 4 15 19 14.3301
44 ford 4 14 17 14.3301
45 ford 4 13 19 14.3301
46 ford 4 13 19 14.3301
47 ford 4 13 17 14.3301
48 ford 4 14 17 14.3301
49 ford 4 14 17 14.3301
50 ford 4 13 16 14.3301
51 ford 4 13 16 14.3301
52 ford 4 13 17 14.3301
53 ford 4 11 15 14.3301
54 ford 4 13 17 14.3301
55 jeep 4 17 22 14.3301
56 jeep 4 15 19 14.3301
57 jeep 4 15 20 14.3301
58 jeep 4 14 17 14.3301
59 jeep 4 9 12 14.3301
60 jeep 4 14 19 14.3301
61 jeep 4 13 18 14.3301
62 jeep 4 11 14 14.3301
63 land rover 4 11 15 14.3301
64 land rover 4 12 18 14.3301
65 land rover 4 12 18 14.3301
66 land rover 4 11 15 14.3301
67 mercury 4 14 17 14.3301
68 mercury 4 13 19 14.3301
69 mercury 4 13 19 14.3301
70 mercury 4 13 17 14.3301
71 nissan 4 14 17 14.3301
72 nissan 4 15 17 14.3301
73 nissan 4 14 20 14.3301
74 nissan 4 12 18 14.3301
75 subaru 4 18 25 14.3301
76 subaru 4 18 24 14.3301
77 subaru 4 20 27 14.3301
78 subaru 4 19 25 14.3301
79 subaru 4 20 26 14.3301
80 subaru 4 18 23 14.3301
81 subaru 4 21 26 14.3301
82 subaru 4 19 26 14.3301
83 subaru 4 19 26 14.3301
84 subaru 4 19 26 14.3301
85 subaru 4 20 25 14.3301
86 subaru 4 20 27 14.3301
87 subaru 4 19 25 14.3301
88 subaru 4 20 27 14.3301
89 toyota 4 15 20 14.3301
90 toyota 4 16 20 14.3301
91 toyota 4 15 19 14.3301
92 toyota 4 15 17 14.3301
93 toyota 4 16 20 14.3301
94 toyota 4 14 17 14.3301
95 toyota 4 11 15 14.3301
96 toyota 4 13 18 14.3301
97 toyota 4 15 20 14.3301
98 toyota 4 16 20 14.3301
99 toyota 4 17 22 14.3301
100 toyota 4 15 17 14.3301
101 toyota 4 15 19 14.3301
102 toyota 4 15 18 14.3301
103 toyota 4 16 20 14.3301
104 audi f 18 29 19.9717
105 audi f 21 29 19.9717
106 audi f 20 31 19.9717
107 audi f 21 30 19.9717
108 audi f 16 26 19.9717
109 audi f 18 26 19.9717
110 audi f 18 27 19.9717
111 chevrolet f 19 27 19.9717
112 chevrolet f 22 30 19.9717
113 chevrolet f 18 26 19.9717
114 chevrolet f 18 29 19.9717
115 chevrolet f 17 26 19.9717
116 dodge f 18 24 19.9717
117 dodge f 17 24 19.9717
118 dodge f 16 22 19.9717
119 dodge f 16 22 19.9717
120 dodge f 17 24 19.9717
121 dodge f 17 24 19.9717
122 dodge f 11 17 19.9717
123 dodge f 15 22 19.9717
124 dodge f 15 21 19.9717
125 dodge f 16 23 19.9717
126 dodge f 16 23 19.9717
127 honda f 28 33 19.9717
128 honda f 24 32 19.9717
129 honda f 25 32 19.9717
130 honda f 23 29 19.9717
131 honda f 24 32 19.9717
132 honda f 26 34 19.9717
133 honda f 25 36 19.9717
134 honda f 24 36 19.9717
135 honda f 21 29 19.9717
136 hyundai f 18 26 19.9717
137 hyundai f 18 27 19.9717
138 hyundai f 21 30 19.9717
139 hyundai f 21 31 19.9717
140 hyundai f 18 26 19.9717
141 hyundai f 18 26 19.9717
142 hyundai f 19 28 19.9717
143 hyundai f 19 26 19.9717
144 hyundai f 19 29 19.9717
145 hyundai f 20 28 19.9717
146 hyundai f 20 27 19.9717
147 hyundai f 17 24 19.9717
148 hyundai f 16 24 19.9717
149 hyundai f 17 24 19.9717
150 nissan f 21 29 19.9717
151 nissan f 19 27 19.9717
152 nissan f 23 31 19.9717
153 nissan f 23 32 19.9717
154 nissan f 19 27 19.9717
155 nissan f 19 26 19.9717
156 nissan f 18 26 19.9717
157 nissan f 19 25 19.9717
158 nissan f 19 25 19.9717
159 pontiac f 18 26 19.9717
160 pontiac f 16 26 19.9717
161 pontiac f 17 27 19.9717
162 pontiac f 18 28 19.9717
163 pontiac f 16 25 19.9717
164 toyota f 21 29 19.9717
165 toyota f 21 27 19.9717
166 toyota f 21 31 19.9717
167 toyota f 21 31 19.9717
168 toyota f 18 26 19.9717
169 toyota f 18 26 19.9717
170 toyota f 19 28 19.9717
171 toyota f 21 27 19.9717
172 toyota f 21 29 19.9717
173 toyota f 21 31 19.9717
174 toyota f 22 31 19.9717
175 toyota f 18 26 19.9717
176 toyota f 18 26 19.9717
177 toyota f 18 27 19.9717
178 toyota f 24 30 19.9717
179 toyota f 24 33 19.9717
180 toyota f 26 35 19.9717
181 toyota f 28 37 19.9717
182 toyota f 26 35 19.9717
183 volkswagen f 21 29 19.9717
184 volkswagen f 19 26 19.9717
185 volkswagen f 21 29 19.9717
186 volkswagen f 22 29 19.9717
187 volkswagen f 17 24 19.9717
188 volkswagen f 33 44 19.9717
189 volkswagen f 21 29 19.9717
190 volkswagen f 19 26 19.9717
191 volkswagen f 22 29 19.9717
192 volkswagen f 21 29 19.9717
193 volkswagen f 21 29 19.9717
194 volkswagen f 21 29 19.9717
195 volkswagen f 16 23 19.9717
196 volkswagen f 17 24 19.9717
197 volkswagen f 35 44 19.9717
198 volkswagen f 29 41 19.9717
199 volkswagen f 21 29 19.9717
200 volkswagen f 19 26 19.9717
201 volkswagen f 20 28 19.9717
202 volkswagen f 20 29 19.9717
203 volkswagen f 21 29 19.9717
204 volkswagen f 18 29 19.9717
205 volkswagen f 19 28 19.9717
206 volkswagen f 21 29 19.9717
207 volkswagen f 16 26 19.9717
208 volkswagen f 18 26 19.9717
209 volkswagen f 17 26 19.9717
210 chevrolet r 14 20 14.0800
211 chevrolet r 11 15 14.0800
212 chevrolet r 14 20 14.0800
213 chevrolet r 13 17 14.0800
214 chevrolet r 12 17 14.0800
215 chevrolet r 16 26 14.0800
216 chevrolet r 15 23 14.0800
217 chevrolet r 16 26 14.0800
218 chevrolet r 15 25 14.0800
219 chevrolet r 15 24 14.0800
220 ford r 11 17 14.0800
221 ford r 11 17 14.0800
222 ford r 12 18 14.0800
223 ford r 18 26 14.0800
224 ford r 18 25 14.0800
225 ford r 17 26 14.0800
226 ford r 16 24 14.0800
227 ford r 15 21 14.0800
228 ford r 15 22 14.0800
229 ford r 15 23 14.0800
230 ford r 15 22 14.0800
231 ford r 14 20 14.0800
232 lincoln r 11 17 14.0800
233 lincoln r 11 16 14.0800
234 lincoln r 12 18 14.0800# transform is very useful standardizing/normalizing
ddply(data, .(drv), transform, delta = mean(cty)-cty) manufacturer drv cty hwy delta
1 audi 4 18 26 -3.66990291
2 audi 4 16 25 -1.66990291
3 audi 4 20 28 -5.66990291
4 audi 4 19 27 -4.66990291
5 audi 4 15 25 -0.66990291
6 audi 4 17 25 -2.66990291
7 audi 4 17 25 -2.66990291
8 audi 4 15 25 -0.66990291
9 audi 4 15 24 -0.66990291
10 audi 4 17 25 -2.66990291
11 audi 4 16 23 -1.66990291
12 chevrolet 4 14 19 0.33009709
13 chevrolet 4 11 14 3.33009709
14 chevrolet 4 11 15 3.33009709
15 chevrolet 4 14 17 0.33009709
16 dodge 4 15 19 -0.66990291
17 dodge 4 14 18 0.33009709
18 dodge 4 13 17 1.33009709
19 dodge 4 14 17 0.33009709
20 dodge 4 14 19 0.33009709
21 dodge 4 14 19 0.33009709
22 dodge 4 9 12 5.33009709
23 dodge 4 11 17 3.33009709
24 dodge 4 11 15 3.33009709
25 dodge 4 13 17 1.33009709
26 dodge 4 13 17 1.33009709
27 dodge 4 9 12 5.33009709
28 dodge 4 13 17 1.33009709
29 dodge 4 11 16 3.33009709
30 dodge 4 13 18 1.33009709
31 dodge 4 11 15 3.33009709
32 dodge 4 12 16 2.33009709
33 dodge 4 9 12 5.33009709
34 dodge 4 13 17 1.33009709
35 dodge 4 13 17 1.33009709
36 dodge 4 12 16 2.33009709
37 dodge 4 9 12 5.33009709
38 dodge 4 11 15 3.33009709
39 dodge 4 11 16 3.33009709
40 dodge 4 13 17 1.33009709
41 dodge 4 11 15 3.33009709
42 ford 4 14 17 0.33009709
43 ford 4 15 19 -0.66990291
44 ford 4 14 17 0.33009709
45 ford 4 13 19 1.33009709
46 ford 4 13 19 1.33009709
47 ford 4 13 17 1.33009709
48 ford 4 14 17 0.33009709
49 ford 4 14 17 0.33009709
50 ford 4 13 16 1.33009709
51 ford 4 13 16 1.33009709
52 ford 4 13 17 1.33009709
53 ford 4 11 15 3.33009709
54 ford 4 13 17 1.33009709
55 jeep 4 17 22 -2.66990291
56 jeep 4 15 19 -0.66990291
57 jeep 4 15 20 -0.66990291
58 jeep 4 14 17 0.33009709
59 jeep 4 9 12 5.33009709
60 jeep 4 14 19 0.33009709
61 jeep 4 13 18 1.33009709
62 jeep 4 11 14 3.33009709
63 land rover 4 11 15 3.33009709
64 land rover 4 12 18 2.33009709
65 land rover 4 12 18 2.33009709
66 land rover 4 11 15 3.33009709
67 mercury 4 14 17 0.33009709
68 mercury 4 13 19 1.33009709
69 mercury 4 13 19 1.33009709
70 mercury 4 13 17 1.33009709
71 nissan 4 14 17 0.33009709
72 nissan 4 15 17 -0.66990291
73 nissan 4 14 20 0.33009709
74 nissan 4 12 18 2.33009709
75 subaru 4 18 25 -3.66990291
76 subaru 4 18 24 -3.66990291
77 subaru 4 20 27 -5.66990291
78 subaru 4 19 25 -4.66990291
79 subaru 4 20 26 -5.66990291
80 subaru 4 18 23 -3.66990291
81 subaru 4 21 26 -6.66990291
82 subaru 4 19 26 -4.66990291
83 subaru 4 19 26 -4.66990291
84 subaru 4 19 26 -4.66990291
85 subaru 4 20 25 -5.66990291
86 subaru 4 20 27 -5.66990291
87 subaru 4 19 25 -4.66990291
88 subaru 4 20 27 -5.66990291
89 toyota 4 15 20 -0.66990291
90 toyota 4 16 20 -1.66990291
91 toyota 4 15 19 -0.66990291
92 toyota 4 15 17 -0.66990291
93 toyota 4 16 20 -1.66990291
94 toyota 4 14 17 0.33009709
95 toyota 4 11 15 3.33009709
96 toyota 4 13 18 1.33009709
97 toyota 4 15 20 -0.66990291
98 toyota 4 16 20 -1.66990291
99 toyota 4 17 22 -2.66990291
100 toyota 4 15 17 -0.66990291
101 toyota 4 15 19 -0.66990291
102 toyota 4 15 18 -0.66990291
103 toyota 4 16 20 -1.66990291
104 audi f 18 29 1.97169811
105 audi f 21 29 -1.02830189
106 audi f 20 31 -0.02830189
107 audi f 21 30 -1.02830189
108 audi f 16 26 3.97169811
109 audi f 18 26 1.97169811
110 audi f 18 27 1.97169811
111 chevrolet f 19 27 0.97169811
112 chevrolet f 22 30 -2.02830189
113 chevrolet f 18 26 1.97169811
114 chevrolet f 18 29 1.97169811
115 chevrolet f 17 26 2.97169811
116 dodge f 18 24 1.97169811
117 dodge f 17 24 2.97169811
118 dodge f 16 22 3.97169811
119 dodge f 16 22 3.97169811
120 dodge f 17 24 2.97169811
121 dodge f 17 24 2.97169811
122 dodge f 11 17 8.97169811
123 dodge f 15 22 4.97169811
124 dodge f 15 21 4.97169811
125 dodge f 16 23 3.97169811
126 dodge f 16 23 3.97169811
127 honda f 28 33 -8.02830189
128 honda f 24 32 -4.02830189
129 honda f 25 32 -5.02830189
130 honda f 23 29 -3.02830189
131 honda f 24 32 -4.02830189
132 honda f 26 34 -6.02830189
133 honda f 25 36 -5.02830189
134 honda f 24 36 -4.02830189
135 honda f 21 29 -1.02830189
136 hyundai f 18 26 1.97169811
137 hyundai f 18 27 1.97169811
138 hyundai f 21 30 -1.02830189
139 hyundai f 21 31 -1.02830189
140 hyundai f 18 26 1.97169811
141 hyundai f 18 26 1.97169811
142 hyundai f 19 28 0.97169811
143 hyundai f 19 26 0.97169811
144 hyundai f 19 29 0.97169811
145 hyundai f 20 28 -0.02830189
146 hyundai f 20 27 -0.02830189
147 hyundai f 17 24 2.97169811
148 hyundai f 16 24 3.97169811
149 hyundai f 17 24 2.97169811
150 nissan f 21 29 -1.02830189
151 nissan f 19 27 0.97169811
152 nissan f 23 31 -3.02830189
153 nissan f 23 32 -3.02830189
154 nissan f 19 27 0.97169811
155 nissan f 19 26 0.97169811
156 nissan f 18 26 1.97169811
157 nissan f 19 25 0.97169811
158 nissan f 19 25 0.97169811
159 pontiac f 18 26 1.97169811
160 pontiac f 16 26 3.97169811
161 pontiac f 17 27 2.97169811
162 pontiac f 18 28 1.97169811
163 pontiac f 16 25 3.97169811
164 toyota f 21 29 -1.02830189
165 toyota f 21 27 -1.02830189
166 toyota f 21 31 -1.02830189
167 toyota f 21 31 -1.02830189
168 toyota f 18 26 1.97169811
169 toyota f 18 26 1.97169811
170 toyota f 19 28 0.97169811
171 toyota f 21 27 -1.02830189
172 toyota f 21 29 -1.02830189
173 toyota f 21 31 -1.02830189
174 toyota f 22 31 -2.02830189
175 toyota f 18 26 1.97169811
176 toyota f 18 26 1.97169811
177 toyota f 18 27 1.97169811
178 toyota f 24 30 -4.02830189
179 toyota f 24 33 -4.02830189
180 toyota f 26 35 -6.02830189
181 toyota f 28 37 -8.02830189
182 toyota f 26 35 -6.02830189
183 volkswagen f 21 29 -1.02830189
184 volkswagen f 19 26 0.97169811
185 volkswagen f 21 29 -1.02830189
186 volkswagen f 22 29 -2.02830189
187 volkswagen f 17 24 2.97169811
188 volkswagen f 33 44 -13.02830189
189 volkswagen f 21 29 -1.02830189
190 volkswagen f 19 26 0.97169811
191 volkswagen f 22 29 -2.02830189
192 volkswagen f 21 29 -1.02830189
193 volkswagen f 21 29 -1.02830189
194 volkswagen f 21 29 -1.02830189
195 volkswagen f 16 23 3.97169811
196 volkswagen f 17 24 2.97169811
197 volkswagen f 35 44 -15.02830189
198 volkswagen f 29 41 -9.02830189
199 volkswagen f 21 29 -1.02830189
200 volkswagen f 19 26 0.97169811
201 volkswagen f 20 28 -0.02830189
202 volkswagen f 20 29 -0.02830189
203 volkswagen f 21 29 -1.02830189
204 volkswagen f 18 29 1.97169811
205 volkswagen f 19 28 0.97169811
206 volkswagen f 21 29 -1.02830189
207 volkswagen f 16 26 3.97169811
208 volkswagen f 18 26 1.97169811
209 volkswagen f 17 26 2.97169811
210 chevrolet r 14 20 0.08000000
211 chevrolet r 11 15 3.08000000
212 chevrolet r 14 20 0.08000000
213 chevrolet r 13 17 1.08000000
214 chevrolet r 12 17 2.08000000
215 chevrolet r 16 26 -1.92000000
216 chevrolet r 15 23 -0.92000000
217 chevrolet r 16 26 -1.92000000
218 chevrolet r 15 25 -0.92000000
219 chevrolet r 15 24 -0.92000000
220 ford r 11 17 3.08000000
221 ford r 11 17 3.08000000
222 ford r 12 18 2.08000000
223 ford r 18 26 -3.92000000
224 ford r 18 25 -3.92000000
225 ford r 17 26 -2.92000000
226 ford r 16 24 -1.92000000
227 ford r 15 21 -0.92000000
228 ford r 15 22 -0.92000000
229 ford r 15 23 -0.92000000
230 ford r 15 22 -0.92000000
231 ford r 14 20 0.08000000
232 lincoln r 11 17 3.08000000
233 lincoln r 11 16 3.08000000
234 lincoln r 12 18 2.08000000# Now let's use plyr to run a simple loop
# We'll ask the question: Does city mpg differ between car manufacturers, for each class of drivetrains (4x4, forward, or rear-wheel drive)? Let's try to automate these ANOVAs and extract the F-statistics and P-values from the ANOVAs.
# Step1: create function to run ANOVA
model <- function(data) { aov(cty~manufacturer, data=data) }
# Step 2: Use plyr to run model for each and create list (called anova.output) to store output for each drivetrain. For dlply, the syntax means d for input data is data frame and l for output data is list.
anova.output <- dlply(data, .(drv), model)
# Step 3: Create function that tells R where to find F-statistic and P-value in the output within the list. The output is somewhat hidden in this example- don't worry about the messy indexing here-- what's important is that this just tells R where the F-stats and P-values are stored.
juicy <- function(x) { c(summary(x)[[1]][["F value"]][[1]],
summary(x)[[1]][["Pr(>F)"]][[1]]) }
# Step 4: Extract components of model output from the list created in previous step. For ldply, the syntax is: input is list and output is data frame. Note that since the input is a list, we don't have to indicate the 2nd parameter (which variable(s) to apply the function to, as the default is to apply function to all elements of the list.)
ldply(anova.output, juicy) drv V1 V2
1 4 30.419370 4.992020e-24
2 f 7.231693 1.741008e-07
3 r 3.432065 5.043200e-02# The data frame shows F-statistics (V1) and P-values (V2) for the ANOVAs by drivetrain.
# We could always condense some of the above steps as well:
anova.output <- dlply(data, .(drv), function(data) aov(cty~manufacturer, data=data))
ldply(anova.output, function(x) { c(summary(x)[[1]][["F value"]][[1]], summary(x)[[1]][["Pr(>F)"]][[1]]) }) drv V1 V2
1 4 30.419370 4.992020e-24
2 f 7.231693 1.741008e-07
3 r 3.432065 5.043200e-02# Note that there are many shortcuts that plyr uses, such as the functions colwis(), each() and splat(). You can always refer to the original article: http://www.jstatsoft.org/v40/i01/ for more on this.Using gapminder dataset
library(gapminder)
library(plyr)
library(ggplot2)
data(gapminder)
str(gapminder)Classes 'tbl_df', 'tbl' and 'data.frame': 1704 obs. of 6 variables:
$ country : Factor w/ 142 levels "Afghanistan",..: 1 1 1 1 1 1 1 1 1 1 ...
$ continent: Factor w/ 5 levels "Africa","Americas",..: 3 3 3 3 3 3 3 3 3 3 ...
$ year : int 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 ...
$ lifeExp : num 28.8 30.3 32 34 36.1 ...
$ pop : int 8425333 9240934 10267083 11537966 13079460 14880372 12881816 13867957 16317921 22227415 ...
$ gdpPercap: num 779 821 853 836 740 ...head(gapminder)# A tibble: 6 x 6
country continent year lifeExp pop gdpPercap
<fct> <fct> <int> <dbl> <int> <dbl>
1 Afghanistan Asia 1952 28.8 8425333 779.
2 Afghanistan Asia 1957 30.3 9240934 821.
3 Afghanistan Asia 1962 32.0 10267083 853.
4 Afghanistan Asia 1967 34.0 11537966 836.
5 Afghanistan Asia 1972 36.1 13079460 740.
6 Afghanistan Asia 1977 38.4 14880372 786.We want to know the number of countries
library(gapminder)
library(plyr)
library(ggplot2)
library(dplyr)
library(DT)
library(plotly)
data(gapminder)
get.n.countries<-function(x)length(unique(x$country))
#use daply
gap_country<-daply(gapminder,.(continent),get.n.countries)
gap_country Africa Americas Asia Europe Oceania
52 25 33 30 2 #use ddply
gap_country<-ddply(gapminder,.(continent),function(x)length(unique(x$country)))
gap_country continent V1
1 Africa 52
2 Americas 25
3 Asia 33
4 Europe 30
5 Oceania 2#use ddply and plot a bargraph
gap_country<-ddply(gapminder,.(continent),summarise,totalcountries=length(unique(country)))
gap_country continent totalcountries
1 Africa 52
2 Americas 25
3 Asia 33
4 Europe 30
5 Oceania 2g<-ggplot(gap_country,aes(x=continent,y=totalcountries,fill=continent))+geom_bar(stat="identity")+
geom_text(aes(label=gap_country$totalcountries),nudge_y=1)+xlab("Continent")+ylab("Total Countries")+
ggtitle("Barplot of Total Countries in each Continent")
ggplotly(g)Sum total population in a dataframe
library(gapminder)
library(plyr)
library(ggplot2)
library(dplyr)
library(DT)
data(gapminder)
#first write the function
get.total.pop<-function(x)sum(x$pop)
#use daply
gap_pop<-daply(gapminder,.(continent),get.total.pop)
gap_pop Africa Americas Asia Europe Oceania
6187585961 7351438499 30507333901 6181115304 212992136 #use ddply
gap_pop<-ddply(gapminder,.(continent),get.total.pop)
gap_pop continent V1
1 Africa 6187585961
2 Americas 7351438499
3 Asia 30507333901
4 Europe 6181115304
5 Oceania 212992136#We note that the pop has been exagerated so we add another variable to the list of splitting criteria.
gap_pop<-ddply(gapminder,.(continent,year),get.total.pop)
gap_pop continent year V1
1 Africa 1952 237640501
2 Africa 1957 264837738
3 Africa 1962 296516865
4 Africa 1967 335289489
5 Africa 1972 379879541
6 Africa 1977 433061021
7 Africa 1982 499348587
8 Africa 1987 574834110
9 Africa 1992 659081517
10 Africa 1997 743832984
11 Africa 2002 833723916
12 Africa 2007 929539692
13 Americas 1952 345152446
14 Americas 1957 386953916
15 Americas 1962 433270254
16 Americas 1967 480746623
17 Americas 1972 529384210
18 Americas 1977 578067699
19 Americas 1982 630290920
20 Americas 1987 682753971
21 Americas 1992 739274104
22 Americas 1997 796900410
23 Americas 2002 849772762
24 Americas 2007 898871184
25 Asia 1952 1395357351
26 Asia 1957 1562780599
27 Asia 1962 1696357182
28 Asia 1967 1905662900
29 Asia 1972 2150972248
30 Asia 1977 2384513556
31 Asia 1982 2610135582
32 Asia 1987 2871220762
33 Asia 1992 3133292191
34 Asia 1997 3383285500
35 Asia 2002 3601802203
36 Asia 2007 3811953827
37 Europe 1952 418120846
38 Europe 1957 437890351
39 Europe 1962 460355155
40 Europe 1967 481178958
41 Europe 1972 500635059
42 Europe 1977 517164531
43 Europe 1982 531266901
44 Europe 1987 543094160
45 Europe 1992 558142797
46 Europe 1997 568944148
47 Europe 2002 578223869
48 Europe 2007 586098529
49 Oceania 1952 10686006
50 Oceania 1957 11941976
51 Oceania 1962 13283518
52 Oceania 1967 14600414
53 Oceania 1972 16106100
54 Oceania 1977 17239000
55 Oceania 1982 18394850
56 Oceania 1987 19574415
57 Oceania 1992 20919651
58 Oceania 1997 22241430
59 Oceania 2002 23454829
60 Oceania 2007 24549947Maximum gdpPercap on each continent
library(gapminder)
library(plyr)
library(ggplot2)
library(dplyr)
library(DT)
data(gapminder)
max_gdp<-ddply(gapminder,.(continent,year),function(x)max(x$gdpPercap))
max_gdp continent year V1
1 Africa 1952 4725.296
2 Africa 1957 5487.104
3 Africa 1962 6757.031
4 Africa 1967 18772.752
5 Africa 1972 21011.497
6 Africa 1977 21951.212
7 Africa 1982 17364.275
8 Africa 1987 11864.408
9 Africa 1992 13522.158
10 Africa 1997 14722.842
11 Africa 2002 12521.714
12 Africa 2007 13206.485
13 Americas 1952 13990.482
14 Americas 1957 14847.127
15 Americas 1962 16173.146
16 Americas 1967 19530.366
17 Americas 1972 21806.036
18 Americas 1977 24072.632
19 Americas 1982 25009.559
20 Americas 1987 29884.350
21 Americas 1992 32003.932
22 Americas 1997 35767.433
23 Americas 2002 39097.100
24 Americas 2007 42951.653
25 Asia 1952 108382.353
26 Asia 1957 113523.133
27 Asia 1962 95458.112
28 Asia 1967 80894.883
29 Asia 1972 109347.867
30 Asia 1977 59265.477
31 Asia 1982 33693.175
32 Asia 1987 28118.430
33 Asia 1992 34932.920
34 Asia 1997 40300.620
35 Asia 2002 36023.105
36 Asia 2007 47306.990
37 Europe 1952 14734.233
38 Europe 1957 17909.490
39 Europe 1962 20431.093
40 Europe 1967 22966.144
41 Europe 1972 27195.113
42 Europe 1977 26982.291
43 Europe 1982 28397.715
44 Europe 1987 31540.975
45 Europe 1992 33965.661
46 Europe 1997 41283.164
47 Europe 2002 44683.975
48 Europe 2007 49357.190
49 Oceania 1952 10556.576
50 Oceania 1957 12247.395
51 Oceania 1962 13175.678
52 Oceania 1967 14526.125
53 Oceania 1972 16788.629
54 Oceania 1977 18334.198
55 Oceania 1982 19477.009
56 Oceania 1987 21888.889
57 Oceania 1992 23424.767
58 Oceania 1997 26997.937
59 Oceania 2002 30687.755
60 Oceania 2007 34435.367DT::datatable(max_gdp,options=list(pageLength=10))An example returning a list
library(gapminder)
library(plyr)
library(ggplot2)
library(dplyr)
library(DT)
data(gapminder)
get.countries<-function(x)unique(x$country)
#use dlply
countries<-dlply(gapminder,.(continent),function(x)unique(x$country))
countries$Africa
[1] Algeria Angola Benin
[4] Botswana Burkina Faso Burundi
[7] Cameroon Central African Republic Chad
[10] Comoros Congo, Dem. Rep. Congo, Rep.
[13] Cote d'Ivoire Djibouti Egypt
[16] Equatorial Guinea Eritrea Ethiopia
[19] Gabon Gambia Ghana
[22] Guinea Guinea-Bissau Kenya
[25] Lesotho Liberia Libya
[28] Madagascar Malawi Mali
[31] Mauritania Mauritius Morocco
[34] Mozambique Namibia Niger
[37] Nigeria Reunion Rwanda
[40] Sao Tome and Principe Senegal Sierra Leone
[43] Somalia South Africa Sudan
[46] Swaziland Tanzania Togo
[49] Tunisia Uganda Zambia
[52] Zimbabwe
142 Levels: Afghanistan Albania Algeria Angola Argentina Australia ... Zimbabwe
$Americas
[1] Argentina Bolivia Brazil
[4] Canada Chile Colombia
[7] Costa Rica Cuba Dominican Republic
[10] Ecuador El Salvador Guatemala
[13] Haiti Honduras Jamaica
[16] Mexico Nicaragua Panama
[19] Paraguay Peru Puerto Rico
[22] Trinidad and Tobago United States Uruguay
[25] Venezuela
142 Levels: Afghanistan Albania Algeria Angola Argentina Australia ... Zimbabwe
$Asia
[1] Afghanistan Bahrain Bangladesh Cambodia
[5] China Hong Kong, China India Indonesia
[9] Iran Iraq Israel Japan
[13] Jordan Korea, Dem. Rep. Korea, Rep. Kuwait
[17] Lebanon Malaysia Mongolia Myanmar
[21] Nepal Oman Pakistan Philippines
[25] Saudi Arabia Singapore Sri Lanka Syria
[29] Taiwan Thailand Vietnam West Bank and Gaza
[33] Yemen, Rep.
142 Levels: Afghanistan Albania Algeria Angola Argentina Australia ... Zimbabwe
$Europe
[1] Albania Austria Belgium
[4] Bosnia and Herzegovina Bulgaria Croatia
[7] Czech Republic Denmark Finland
[10] France Germany Greece
[13] Hungary Iceland Ireland
[16] Italy Montenegro Netherlands
[19] Norway Poland Portugal
[22] Romania Serbia Slovak Republic
[25] Slovenia Spain Sweden
[28] Switzerland Turkey United Kingdom
142 Levels: Afghanistan Albania Algeria Angola Argentina Australia ... Zimbabwe
$Oceania
[1] Australia New Zealand
142 Levels: Afghanistan Albania Algeria Angola Argentina Australia ... Zimbabwe
attr(,"split_type")
[1] "data.frame"
attr(,"split_labels")
continent
1 Africa
2 Americas
3 Asia
4 Europe
5 Oceaniacountries<-dlply(gapminder,.(continent),get.countries)
countries$Africa
[1] Algeria Angola Benin
[4] Botswana Burkina Faso Burundi
[7] Cameroon Central African Republic Chad
[10] Comoros Congo, Dem. Rep. Congo, Rep.
[13] Cote d'Ivoire Djibouti Egypt
[16] Equatorial Guinea Eritrea Ethiopia
[19] Gabon Gambia Ghana
[22] Guinea Guinea-Bissau Kenya
[25] Lesotho Liberia Libya
[28] Madagascar Malawi Mali
[31] Mauritania Mauritius Morocco
[34] Mozambique Namibia Niger
[37] Nigeria Reunion Rwanda
[40] Sao Tome and Principe Senegal Sierra Leone
[43] Somalia South Africa Sudan
[46] Swaziland Tanzania Togo
[49] Tunisia Uganda Zambia
[52] Zimbabwe
142 Levels: Afghanistan Albania Algeria Angola Argentina Australia ... Zimbabwe
$Americas
[1] Argentina Bolivia Brazil
[4] Canada Chile Colombia
[7] Costa Rica Cuba Dominican Republic
[10] Ecuador El Salvador Guatemala
[13] Haiti Honduras Jamaica
[16] Mexico Nicaragua Panama
[19] Paraguay Peru Puerto Rico
[22] Trinidad and Tobago United States Uruguay
[25] Venezuela
142 Levels: Afghanistan Albania Algeria Angola Argentina Australia ... Zimbabwe
$Asia
[1] Afghanistan Bahrain Bangladesh Cambodia
[5] China Hong Kong, China India Indonesia
[9] Iran Iraq Israel Japan
[13] Jordan Korea, Dem. Rep. Korea, Rep. Kuwait
[17] Lebanon Malaysia Mongolia Myanmar
[21] Nepal Oman Pakistan Philippines
[25] Saudi Arabia Singapore Sri Lanka Syria
[29] Taiwan Thailand Vietnam West Bank and Gaza
[33] Yemen, Rep.
142 Levels: Afghanistan Albania Algeria Angola Argentina Australia ... Zimbabwe
$Europe
[1] Albania Austria Belgium
[4] Bosnia and Herzegovina Bulgaria Croatia
[7] Czech Republic Denmark Finland
[10] France Germany Greece
[13] Hungary Iceland Ireland
[16] Italy Montenegro Netherlands
[19] Norway Poland Portugal
[22] Romania Serbia Slovak Republic
[25] Slovenia Spain Sweden
[28] Switzerland Turkey United Kingdom
142 Levels: Afghanistan Albania Algeria Angola Argentina Australia ... Zimbabwe
$Oceania
[1] Australia New Zealand
142 Levels: Afghanistan Albania Algeria Angola Argentina Australia ... Zimbabwe
attr(,"split_type")
[1] "data.frame"
attr(,"split_labels")
continent
1 Africa
2 Americas
3 Asia
4 Europe
5 OceaniaFeed data into a model one by one returning fits to a list of models
- First see if you can write a function that given a data frame x fits a model to data.
library(gapminder)
library(plyr)
library(ggplot2)
library(dplyr)
library(DT)
data(gapminder)
model<-function(x){
lm(lifeExp~log10(gdpPercap),data=gapminder)
}
#Now let's try it on a subset of data
fit<-model(data[data$year==1982 & data$continent=="Asia",])
fit
Call:
lm(formula = lifeExp ~ log10(gdpPercap), data = gapminder)
Coefficients:
(Intercept) log10(gdpPercap)
-9.101 19.353 # Now let's apply it all continents in all years
fitted.linear.model<-dlply(gapminder,.(continent,year),model)
coef(fitted.linear.model[[1]]) (Intercept) log10(gdpPercap)
-9.100889 19.353423 ldply(fitted.linear.model,coef) continent year (Intercept) log10(gdpPercap)
1 Africa 1952 -9.100889 19.35342
2 Africa 1957 -9.100889 19.35342
3 Africa 1962 -9.100889 19.35342
4 Africa 1967 -9.100889 19.35342
5 Africa 1972 -9.100889 19.35342
6 Africa 1977 -9.100889 19.35342
7 Africa 1982 -9.100889 19.35342
8 Africa 1987 -9.100889 19.35342
9 Africa 1992 -9.100889 19.35342
10 Africa 1997 -9.100889 19.35342
11 Africa 2002 -9.100889 19.35342
12 Africa 2007 -9.100889 19.35342
13 Americas 1952 -9.100889 19.35342
14 Americas 1957 -9.100889 19.35342
15 Americas 1962 -9.100889 19.35342
16 Americas 1967 -9.100889 19.35342
17 Americas 1972 -9.100889 19.35342
18 Americas 1977 -9.100889 19.35342
19 Americas 1982 -9.100889 19.35342
20 Americas 1987 -9.100889 19.35342
21 Americas 1992 -9.100889 19.35342
22 Americas 1997 -9.100889 19.35342
23 Americas 2002 -9.100889 19.35342
24 Americas 2007 -9.100889 19.35342
25 Asia 1952 -9.100889 19.35342
26 Asia 1957 -9.100889 19.35342
27 Asia 1962 -9.100889 19.35342
28 Asia 1967 -9.100889 19.35342
29 Asia 1972 -9.100889 19.35342
30 Asia 1977 -9.100889 19.35342
31 Asia 1982 -9.100889 19.35342
32 Asia 1987 -9.100889 19.35342
33 Asia 1992 -9.100889 19.35342
34 Asia 1997 -9.100889 19.35342
35 Asia 2002 -9.100889 19.35342
36 Asia 2007 -9.100889 19.35342
37 Europe 1952 -9.100889 19.35342
38 Europe 1957 -9.100889 19.35342
39 Europe 1962 -9.100889 19.35342
40 Europe 1967 -9.100889 19.35342
41 Europe 1972 -9.100889 19.35342
42 Europe 1977 -9.100889 19.35342
43 Europe 1982 -9.100889 19.35342
44 Europe 1987 -9.100889 19.35342
45 Europe 1992 -9.100889 19.35342
46 Europe 1997 -9.100889 19.35342
47 Europe 2002 -9.100889 19.35342
48 Europe 2007 -9.100889 19.35342
49 Oceania 1952 -9.100889 19.35342
50 Oceania 1957 -9.100889 19.35342
51 Oceania 1962 -9.100889 19.35342
52 Oceania 1967 -9.100889 19.35342
53 Oceania 1972 -9.100889 19.35342
54 Oceania 1977 -9.100889 19.35342
55 Oceania 1982 -9.100889 19.35342
56 Oceania 1987 -9.100889 19.35342
57 Oceania 1992 -9.100889 19.35342
58 Oceania 1997 -9.100889 19.35342
59 Oceania 2002 -9.100889 19.35342
60 Oceania 2007 -9.100889 19.35342#we want the r2 too
ldply(fitted.linear.model,function(x)summary(x)$r.squared) continent year V1
1 Africa 1952 0.6522466
2 Africa 1957 0.6522466
3 Africa 1962 0.6522466
4 Africa 1967 0.6522466
5 Africa 1972 0.6522466
6 Africa 1977 0.6522466
7 Africa 1982 0.6522466
8 Africa 1987 0.6522466
9 Africa 1992 0.6522466
10 Africa 1997 0.6522466
11 Africa 2002 0.6522466
12 Africa 2007 0.6522466
13 Americas 1952 0.6522466
14 Americas 1957 0.6522466
15 Americas 1962 0.6522466
16 Americas 1967 0.6522466
17 Americas 1972 0.6522466
18 Americas 1977 0.6522466
19 Americas 1982 0.6522466
20 Americas 1987 0.6522466
21 Americas 1992 0.6522466
22 Americas 1997 0.6522466
23 Americas 2002 0.6522466
24 Americas 2007 0.6522466
25 Asia 1952 0.6522466
26 Asia 1957 0.6522466
27 Asia 1962 0.6522466
28 Asia 1967 0.6522466
29 Asia 1972 0.6522466
30 Asia 1977 0.6522466
31 Asia 1982 0.6522466
32 Asia 1987 0.6522466
33 Asia 1992 0.6522466
34 Asia 1997 0.6522466
35 Asia 2002 0.6522466
36 Asia 2007 0.6522466
37 Europe 1952 0.6522466
38 Europe 1957 0.6522466
39 Europe 1962 0.6522466
40 Europe 1967 0.6522466
41 Europe 1972 0.6522466
42 Europe 1977 0.6522466
43 Europe 1982 0.6522466
44 Europe 1987 0.6522466
45 Europe 1992 0.6522466
46 Europe 1997 0.6522466
47 Europe 2002 0.6522466
48 Europe 2007 0.6522466
49 Oceania 1952 0.6522466
50 Oceania 1957 0.6522466
51 Oceania 1962 0.6522466
52 Oceania 1967 0.6522466
53 Oceania 1972 0.6522466
54 Oceania 1977 0.6522466
55 Oceania 1982 0.6522466
56 Oceania 1987 0.6522466
57 Oceania 1992 0.6522466
58 Oceania 1997 0.6522466
59 Oceania 2002 0.6522466
60 Oceania 2007 0.6522466We want model function to return the desired output
library(gapminder)
library(plyr)
library(ggplot2)
library(dplyr)
library(DT)
data(gapminder)
model<-function(x){
fit<-lm(lifeExp~log10(gdpPercap),data=gapminder)
data.frame(n=length(x$lifeExp),r2=summary(fit)$r.squared,a=coef(fit)[[1]],b=coef(fit)[[2]])
}
#Then apply ddply
ddply(gapminder,.(continent,year),model) continent year n r2 a b
1 Africa 1952 52 0.6522466 -9.100889 19.35342
2 Africa 1957 52 0.6522466 -9.100889 19.35342
3 Africa 1962 52 0.6522466 -9.100889 19.35342
4 Africa 1967 52 0.6522466 -9.100889 19.35342
5 Africa 1972 52 0.6522466 -9.100889 19.35342
6 Africa 1977 52 0.6522466 -9.100889 19.35342
7 Africa 1982 52 0.6522466 -9.100889 19.35342
8 Africa 1987 52 0.6522466 -9.100889 19.35342
9 Africa 1992 52 0.6522466 -9.100889 19.35342
10 Africa 1997 52 0.6522466 -9.100889 19.35342
11 Africa 2002 52 0.6522466 -9.100889 19.35342
12 Africa 2007 52 0.6522466 -9.100889 19.35342
13 Americas 1952 25 0.6522466 -9.100889 19.35342
14 Americas 1957 25 0.6522466 -9.100889 19.35342
15 Americas 1962 25 0.6522466 -9.100889 19.35342
16 Americas 1967 25 0.6522466 -9.100889 19.35342
17 Americas 1972 25 0.6522466 -9.100889 19.35342
18 Americas 1977 25 0.6522466 -9.100889 19.35342
19 Americas 1982 25 0.6522466 -9.100889 19.35342
20 Americas 1987 25 0.6522466 -9.100889 19.35342
21 Americas 1992 25 0.6522466 -9.100889 19.35342
22 Americas 1997 25 0.6522466 -9.100889 19.35342
23 Americas 2002 25 0.6522466 -9.100889 19.35342
24 Americas 2007 25 0.6522466 -9.100889 19.35342
25 Asia 1952 33 0.6522466 -9.100889 19.35342
26 Asia 1957 33 0.6522466 -9.100889 19.35342
27 Asia 1962 33 0.6522466 -9.100889 19.35342
28 Asia 1967 33 0.6522466 -9.100889 19.35342
29 Asia 1972 33 0.6522466 -9.100889 19.35342
30 Asia 1977 33 0.6522466 -9.100889 19.35342
31 Asia 1982 33 0.6522466 -9.100889 19.35342
32 Asia 1987 33 0.6522466 -9.100889 19.35342
33 Asia 1992 33 0.6522466 -9.100889 19.35342
34 Asia 1997 33 0.6522466 -9.100889 19.35342
35 Asia 2002 33 0.6522466 -9.100889 19.35342
36 Asia 2007 33 0.6522466 -9.100889 19.35342
37 Europe 1952 30 0.6522466 -9.100889 19.35342
38 Europe 1957 30 0.6522466 -9.100889 19.35342
39 Europe 1962 30 0.6522466 -9.100889 19.35342
40 Europe 1967 30 0.6522466 -9.100889 19.35342
41 Europe 1972 30 0.6522466 -9.100889 19.35342
42 Europe 1977 30 0.6522466 -9.100889 19.35342
43 Europe 1982 30 0.6522466 -9.100889 19.35342
44 Europe 1987 30 0.6522466 -9.100889 19.35342
45 Europe 1992 30 0.6522466 -9.100889 19.35342
46 Europe 1997 30 0.6522466 -9.100889 19.35342
47 Europe 2002 30 0.6522466 -9.100889 19.35342
48 Europe 2007 30 0.6522466 -9.100889 19.35342
49 Oceania 1952 2 0.6522466 -9.100889 19.35342
50 Oceania 1957 2 0.6522466 -9.100889 19.35342
51 Oceania 1962 2 0.6522466 -9.100889 19.35342
52 Oceania 1967 2 0.6522466 -9.100889 19.35342
53 Oceania 1972 2 0.6522466 -9.100889 19.35342
54 Oceania 1977 2 0.6522466 -9.100889 19.35342
55 Oceania 1982 2 0.6522466 -9.100889 19.35342
56 Oceania 1987 2 0.6522466 -9.100889 19.35342
57 Oceania 1992 2 0.6522466 -9.100889 19.35342
58 Oceania 1997 2 0.6522466 -9.100889 19.35342
59 Oceania 2002 2 0.6522466 -9.100889 19.35342
60 Oceania 2007 2 0.6522466 -9.100889 19.35342As a final extension
library(gapminder)
library(plyr)
library(ggplot2)
library(dplyr)
library(DT)
data(gapminder)
model<-function(d,x,y){
fit<-lm(d[[y]]~log10(d[[x]]))
data.frame(n=length(d[[y]]),r2=summary(fit)$r.squared,a=coef(fit)[1],b=coef(fit)[2])
}
ddply(gapminder,.(continent,year),model,y="lifeExp",x="gdpPercap") continent year n r2 a b
1 Africa 1952 52 0.1037812 23.0525953 5.382298
2 Africa 1957 52 0.1148056 22.5199297 6.189243
3 Africa 1962 52 0.1194333 23.7915807 6.346554
4 Africa 1967 52 0.1616558 23.1749106 7.065407
5 Africa 1972 52 0.2380010 19.5713666 8.757875
6 Africa 1977 52 0.3245551 16.3845126 10.384037
7 Africa 1982 52 0.4389610 10.2567015 12.927044
8 Africa 1987 52 0.5101598 5.9844769 14.917624
9 Africa 1992 52 0.4683695 0.1812503 16.884333
10 Africa 1997 52 0.4689442 3.2574767 15.853737
11 Africa 2002 52 0.2657330 15.2988527 11.884759
12 Africa 2007 52 0.2038938 22.9068245 9.811029
13 Americas 1952 25 0.3911997 -31.6862405 24.041040
14 Americas 1957 25 0.4465440 -29.2582342 23.772381
15 Americas 1962 25 0.4761545 -28.1511355 23.930105
16 Americas 1967 25 0.4577251 -13.3036872 20.086358
17 Americas 1972 25 0.4956498 -10.3599462 19.508293
18 Americas 1977 25 0.4678976 -7.2373578 18.914422
19 Americas 1982 25 0.5584315 -7.4120953 19.409076
20 Americas 1987 25 0.6296113 6.1098075 16.344021
21 Americas 1992 25 0.6657980 16.6470136 13.942420
22 Americas 1997 25 0.6316171 23.5244087 12.421122
23 Americas 2002 25 0.6091473 27.7003029 11.639127
24 Americas 2007 25 0.6085279 33.0650797 10.347969
25 Asia 1952 33 0.2629815 15.8323119 9.578001
26 Asia 1957 33 0.2552922 18.0672456 9.604586
27 Asia 1962 33 0.3084034 16.5934165 10.574676
28 Asia 1967 33 0.3295700 19.7785273 10.365823
29 Asia 1972 33 0.3854367 21.9226137 10.234456
30 Asia 1977 33 0.4210308 20.0389431 11.219246
31 Asia 1982 33 0.5035094 23.3557733 11.004645
32 Asia 1987 33 0.6205701 22.0206839 11.914813
33 Asia 1992 33 0.6646179 24.0496786 11.716722
34 Asia 1997 33 0.6816437 24.7325393 11.772759
35 Asia 2002 33 0.6634051 22.7361333 12.533686
36 Asia 2007 33 0.6405559 25.6501149 11.875027
37 Europe 1952 30 0.7482006 -11.9466866 20.732600
38 Europe 1957 30 0.6892937 1.9131575 17.151109
39 Europe 1962 30 0.6317579 15.9752595 13.613689
40 Europe 1967 30 0.6240650 20.3352355 12.503058
41 Europe 1972 30 0.5585790 28.7981541 10.377387
42 Europe 1977 30 0.5572371 29.4891023 10.333333
43 Europe 1982 30 0.5215061 31.0971353 10.062767
44 Europe 1987 30 0.5200620 33.7253780 9.542663
45 Europe 1992 30 0.5932718 41.1793922 8.020074
46 Europe 1997 30 0.6627327 39.1376795 8.656197
47 Europe 2002 30 0.7158133 40.0208847 8.610798
48 Europe 2007 30 0.6986601 35.4389538 9.732777
49 Oceania 1952 2 1.0000000 19.5730133 12.381434
50 Oceania 1957 2 1.0000000 76.1428615 -1.439041
51 Oceania 1962 2 1.0000000 32.3035181 9.451123
52 Oceania 1967 2 1.0000000 1009.0303027 -225.347562
53 Oceania 1972 2 1.0000000 63.3283428 2.035888
54 Oceania 1977 2 1.0000000 -28.9691479 24.033041
55 Oceania 1982 2 1.0000000 -14.6031550 20.828230
56 Oceania 1987 2 1.0000000 -65.2726571 32.623355
57 Oceania 1992 2 1.0000000 26.7228887 11.634071
58 Oceania 1997 2 1.0000000 26.3445137 11.844182
59 Oceania 2002 2 1.0000000 33.9027938 10.356044
60 Oceania 2007 2 1.0000000 46.8057196 7.588549ddply(gapminder,.(continent,year),model,y="lifeExp",x="pop") continent year n r2 a b
1 Africa 1952 52 1.680740e-02 45.82174 -1.061114071
2 Africa 1957 52 1.647705e-02 48.51212 -1.141957809
3 Africa 1962 52 1.357602e-02 50.24610 -1.083610037
4 Africa 1967 52 1.244434e-02 52.25823 -1.074383509
5 Africa 1972 52 1.453069e-02 55.42445 -1.226755888
6 Africa 1977 52 1.543458e-02 58.33067 -1.334948680
7 Africa 1982 52 2.149108e-02 62.95431 -1.716991971
8 Africa 1987 52 2.318606e-02 65.97257 -1.891056448
9 Africa 1992 52 2.338874e-02 69.01281 -2.283980852
10 Africa 1997 52 2.816713e-02 69.96727 -2.411805750
11 Africa 2002 52 1.389859e-02 65.53089 -1.785103203
12 Africa 2007 52 1.021619e-02 65.35258 -1.532099416
13 Americas 1952 25 5.330530e-02 28.99425 3.658742281
14 Americas 1957 25 4.042921e-02 35.28231 3.089795762
15 Americas 1962 25 2.660386e-02 42.40781 2.369378399
16 Americas 1967 25 1.795992e-02 48.04260 1.817967119
17 Americas 1972 25 1.395788e-02 52.22941 1.483622772
18 Americas 1977 25 1.655218e-02 53.64259 1.558937684
19 Americas 1982 25 2.189085e-02 54.39069 1.706533371
20 Americas 1987 25 1.769986e-02 58.83659 1.326233344
21 Americas 1992 25 2.415249e-02 59.89817 1.378012025
22 Americas 1997 25 3.482957e-02 60.13455 1.561720628
23 Americas 2002 25 3.847289e-02 61.02259 1.608888299
24 Americas 2007 25 4.270098e-02 62.48011 1.564414213
25 Asia 1952 33 5.972720e-02 64.45784 -2.653709215
26 Asia 1957 33 3.630124e-02 64.28197 -2.170943716
27 Asia 1962 33 5.613702e-02 71.13631 -2.814381525
28 Asia 1967 33 2.715499e-02 68.35687 -1.951926642
29 Asia 1972 33 2.603796e-02 71.04963 -1.941551914
30 Asia 1977 33 2.409241e-02 73.54362 -1.954630399
31 Asia 1982 33 4.896702e-02 79.92524 -2.409630550
32 Asia 1987 33 5.841097e-02 83.35227 -2.557383185
33 Asia 1992 33 6.491277e-02 85.96943 -2.668992385
34 Asia 1997 33 6.089025e-02 87.34755 -2.636110591
35 Asia 2002 33 5.120642e-02 87.90039 -2.532215613
36 Asia 2007 33 5.608188e-02 89.59822 -2.546747792
37 Europe 1952 30 7.971462e-04 66.47946 -0.303116636
38 Europe 1957 30 2.173609e-03 69.57908 -0.419537642
39 Europe 1962 30 5.695284e-03 72.35425 -0.554674642
40 Europe 1967 30 9.268471e-03 74.06920 -0.627947029
41 Europe 1972 30 2.199767e-02 76.49081 -0.826584155
42 Europe 1977 30 4.558867e-02 79.91976 -1.151515813
43 Europe 1982 30 3.004874e-02 79.52002 -0.966738479
44 Europe 1987 30 2.270296e-02 79.41169 -0.829623099
45 Europe 1992 30 1.699916e-02 79.52509 -0.730052693
46 Europe 1997 30 2.382061e-03 77.35196 -0.264925698
47 Europe 2002 30 7.515142e-05 76.38955 0.044571189
48 Europe 2007 30 6.898088e-07 77.61818 0.004355314
49 Oceania 1952 2 1.000000e+00 72.05117 -0.422414707
50 Oceania 1957 2 1.000000e+00 69.56474 0.109521355
51 Oceania 1962 2 1.000000e+00 74.35128 -0.486446206
52 Oceania 1967 2 1.000000e+00 75.75236 -0.657621449
53 Oceania 1972 2 1.000000e+00 71.49393 0.061248019
54 Oceania 1977 2 1.000000e+00 59.48129 1.959693154
55 Oceania 1982 2 1.000000e+00 65.16196 1.333730586
56 Oceania 1987 2 1.000000e+00 55.42689 2.897375599
57 Oceania 1992 2 1.000000e+00 64.94771 1.741405884
58 Oceania 1997 2 1.000000e+00 65.60108 1.819983594
59 Oceania 2002 2 1.000000e+00 67.22949 1.802273891
60 Oceania 2007 2 1.000000e+00 70.40454 1.481522085- In just 6 lines we can fit about 120 linear models and return two tables summarising these models. This is how powerful plyr is.
Simplifying plots with plyr
library(gapminder)
library(plyr)
library(ggplot2)
library(dplyr)
library(DT)
data(gapminder)
col.table<-c("red","blue","green","orange")
gapminder.1982<-subset(gapminder,year==1982)
add.trend.line<-function(x,y,d,...){
fit<-lm(d[[y]]~log10(d[[x]]))
abline(fit,...)
}
#recall functions and objects created in the functions lesson
colour.by.category<-function(x,table){
unname(table[x])
}
rescale<-function(x,r.out){
p<-(x-min(x))/(max(x)-min(x))
r.out[[1]]+p*(r.out[[2]]-r.out[[1]])
}
col<-colour.by.category(gapminder.1982$continent,col.table)
cex<-rescale(sqrt(gapminder.1982$pop),c(0.2,10))
#now use function add.trend.line
plot(lifeExp~gdpPercap,gapminder.1982,log="x",cex=cex,col=col,pch=21)
d_ply(gapminder.1982,.(continent),function(x)add.trend.line("gdpPercap","lifeExp",x,col=col.table[x$continent]))
- One way to avoid repetition is to pass the add.trend.line function into d_ply. The underscore in d_ply tells us that we don’t want any output, we just want to run the function.
Summarise
- For summaries of the whole dataset, you can call summarise directly.
library(gapminder)
library(plyr)
library(ggplot2)
library(dplyr)
library(DT)
data(gapminder)
summarise(gapminder,pop.mean=mean(pop),pop.var=var(pop),pop.max=max(pop))# A tibble: 1 x 3
pop.mean pop.var pop.max
<dbl> <dbl> <int>
1 29601212. 1.13e16 1318683096#But if you want to split by groups, you need to combine with ddply
ddply(gapminder,.(continent,year),summarise,pop.mean=mean(pop),pop.var=var(pop),pop.max=max(pop)) continent year pop.mean pop.var pop.max
1 Africa 1952 4570010 3.991017e+13 33119096
2 Africa 1957 5093033 5.007037e+13 37173340
3 Africa 1962 5702247 6.332252e+13 41871351
4 Africa 1967 6447875 8.073930e+13 47287752
5 Africa 1972 7305376 1.026338e+14 53740085
6 Africa 1977 8328097 1.342165e+14 62209173
7 Africa 1982 9602857 1.810705e+14 73039376
8 Africa 1987 11054502 2.334015e+14 81551520
9 Africa 1992 12674645 3.084491e+14 93364244
10 Africa 1997 14304480 3.949367e+14 106207839
11 Africa 2002 16033152 4.974237e+14 119901274
12 Africa 2007 17875763 6.208931e+14 135031164
13 Americas 1952 13806098 1.045981e+15 157553000
14 Americas 1957 15478157 1.262883e+15 171984000
15 Americas 1962 17330810 1.511408e+15 186538000
16 Americas 1967 19229865 1.757755e+15 198712000
17 Americas 1972 21175368 2.019196e+15 209896000
18 Americas 1977 23122708 2.294799e+15 220239000
19 Americas 1982 25211637 2.631113e+15 232187835
20 Americas 1987 27310159 2.965858e+15 242803533
21 Americas 1992 29570964 3.376682e+15 256894189
22 Americas 1997 31876016 3.848071e+15 272911760
23 Americas 2002 33990910 4.303563e+15 287675526
24 Americas 2007 35954847 4.738089e+15 301139947
25 Asia 1952 42283556 1.282029e+16 556263527
26 Asia 1957 47356988 1.640861e+16 637408000
27 Asia 1962 51404763 1.852394e+16 665770000
28 Asia 1967 57747361 2.351370e+16 754550000
29 Asia 1972 65180977 3.030903e+16 862030000
30 Asia 1977 72257987 3.675171e+16 943455000
31 Asia 1982 79095018 4.265940e+16 1000281000
32 Asia 1987 87006690 5.095549e+16 1084035000
33 Asia 1992 94948248 6.000559e+16 1164970000
34 Asia 1997 102523803 6.882737e+16 1230075000
35 Asia 2002 109145521 7.656382e+16 1280400000
36 Asia 2007 115513752 8.391068e+16 1318683096
37 Europe 1952 13937362 2.974745e+14 69145952
38 Europe 1957 14596345 3.179928e+14 71019069
39 Europe 1962 15345172 3.480619e+14 73739117
40 Europe 1967 16039299 3.780531e+14 76368453
41 Europe 1972 16687835 4.072461e+14 78717088
42 Europe 1977 17238818 4.227360e+14 78160773
43 Europe 1982 17708897 4.397951e+14 78335266
44 Europe 1987 18103139 4.566646e+14 77718298
45 Europe 1992 18604760 4.895925e+14 80597764
46 Europe 1997 18964805 5.174781e+14 82011073
47 Europe 2002 19274129 5.393398e+14 82350671
48 Europe 2007 19536618 5.581285e+14 82400996
49 Oceania 1952 5343003 2.242101e+13 8691212
50 Oceania 1957 5970988 2.799886e+13 9712569
51 Oceania 1962 6641759 3.449829e+13 10794968
52 Oceania 1967 7300207 4.180741e+13 11872264
53 Oceania 1972 8053050 5.250973e+13 13177000
54 Oceania 1977 8619500 5.950532e+13 14074100
55 Oceania 1982 9197425 7.168295e+13 15184200
56 Oceania 1987 9787208 8.372287e+13 16257249
57 Oceania 1992 10459826 9.862122e+13 17481977
58 Oceania 1997 11120715 1.108420e+14 18565243
59 Oceania 2002 11727415 1.222853e+14 19546792
60 Oceania 2007 12274974 1.331452e+14 20434176- If we are calling each directly, the format is different.
library(gapminder)
library(plyr)
library(ggplot2)
library(dplyr)
library(DT)
data(gapminder)
ddply(gapminder,.(continent,year),function(x)sum(x$pop)) continent year V1
1 Africa 1952 237640501
2 Africa 1957 264837738
3 Africa 1962 296516865
4 Africa 1967 335289489
5 Africa 1972 379879541
6 Africa 1977 433061021
7 Africa 1982 499348587
8 Africa 1987 574834110
9 Africa 1992 659081517
10 Africa 1997 743832984
11 Africa 2002 833723916
12 Africa 2007 929539692
13 Americas 1952 345152446
14 Americas 1957 386953916
15 Americas 1962 433270254
16 Americas 1967 480746623
17 Americas 1972 529384210
18 Americas 1977 578067699
19 Americas 1982 630290920
20 Americas 1987 682753971
21 Americas 1992 739274104
22 Americas 1997 796900410
23 Americas 2002 849772762
24 Americas 2007 898871184
25 Asia 1952 1395357351
26 Asia 1957 1562780599
27 Asia 1962 1696357182
28 Asia 1967 1905662900
29 Asia 1972 2150972248
30 Asia 1977 2384513556
31 Asia 1982 2610135582
32 Asia 1987 2871220762
33 Asia 1992 3133292191
34 Asia 1997 3383285500
35 Asia 2002 3601802203
36 Asia 2007 3811953827
37 Europe 1952 418120846
38 Europe 1957 437890351
39 Europe 1962 460355155
40 Europe 1967 481178958
41 Europe 1972 500635059
42 Europe 1977 517164531
43 Europe 1982 531266901
44 Europe 1987 543094160
45 Europe 1992 558142797
46 Europe 1997 568944148
47 Europe 2002 578223869
48 Europe 2007 586098529
49 Oceania 1952 10686006
50 Oceania 1957 11941976
51 Oceania 1962 13283518
52 Oceania 1967 14600414
53 Oceania 1972 16106100
54 Oceania 1977 17239000
55 Oceania 1982 18394850
56 Oceania 1987 19574415
57 Oceania 1992 20919651
58 Oceania 1997 22241430
59 Oceania 2002 23454829
60 Oceania 2007 24549947ddply(gapminder,.(continent,year),function(x)var(x$pop)) continent year V1
1 Africa 1952 3.991017e+13
2 Africa 1957 5.007037e+13
3 Africa 1962 6.332252e+13
4 Africa 1967 8.073930e+13
5 Africa 1972 1.026338e+14
6 Africa 1977 1.342165e+14
7 Africa 1982 1.810705e+14
8 Africa 1987 2.334015e+14
9 Africa 1992 3.084491e+14
10 Africa 1997 3.949367e+14
11 Africa 2002 4.974237e+14
12 Africa 2007 6.208931e+14
13 Americas 1952 1.045981e+15
14 Americas 1957 1.262883e+15
15 Americas 1962 1.511408e+15
16 Americas 1967 1.757755e+15
17 Americas 1972 2.019196e+15
18 Americas 1977 2.294799e+15
19 Americas 1982 2.631113e+15
20 Americas 1987 2.965858e+15
21 Americas 1992 3.376682e+15
22 Americas 1997 3.848071e+15
23 Americas 2002 4.303563e+15
24 Americas 2007 4.738089e+15
25 Asia 1952 1.282029e+16
26 Asia 1957 1.640861e+16
27 Asia 1962 1.852394e+16
28 Asia 1967 2.351370e+16
29 Asia 1972 3.030903e+16
30 Asia 1977 3.675171e+16
31 Asia 1982 4.265940e+16
32 Asia 1987 5.095549e+16
33 Asia 1992 6.000559e+16
34 Asia 1997 6.882737e+16
35 Asia 2002 7.656382e+16
36 Asia 2007 8.391068e+16
37 Europe 1952 2.974745e+14
38 Europe 1957 3.179928e+14
39 Europe 1962 3.480619e+14
40 Europe 1967 3.780531e+14
41 Europe 1972 4.072461e+14
42 Europe 1977 4.227360e+14
43 Europe 1982 4.397951e+14
44 Europe 1987 4.566646e+14
45 Europe 1992 4.895925e+14
46 Europe 1997 5.174781e+14
47 Europe 2002 5.393398e+14
48 Europe 2007 5.581285e+14
49 Oceania 1952 2.242101e+13
50 Oceania 1957 2.799886e+13
51 Oceania 1962 3.449829e+13
52 Oceania 1967 4.180741e+13
53 Oceania 1972 5.250973e+13
54 Oceania 1977 5.950532e+13
55 Oceania 1982 7.168295e+13
56 Oceania 1987 8.372287e+13
57 Oceania 1992 9.862122e+13
58 Oceania 1997 1.108420e+14
59 Oceania 2002 1.222853e+14
60 Oceania 2007 1.331452e+14ddply(gapminder,.(continent,year),function(x)max(x$pop)) continent year V1
1 Africa 1952 33119096
2 Africa 1957 37173340
3 Africa 1962 41871351
4 Africa 1967 47287752
5 Africa 1972 53740085
6 Africa 1977 62209173
7 Africa 1982 73039376
8 Africa 1987 81551520
9 Africa 1992 93364244
10 Africa 1997 106207839
11 Africa 2002 119901274
12 Africa 2007 135031164
13 Americas 1952 157553000
14 Americas 1957 171984000
15 Americas 1962 186538000
16 Americas 1967 198712000
17 Americas 1972 209896000
18 Americas 1977 220239000
19 Americas 1982 232187835
20 Americas 1987 242803533
21 Americas 1992 256894189
22 Americas 1997 272911760
23 Americas 2002 287675526
24 Americas 2007 301139947
25 Asia 1952 556263527
26 Asia 1957 637408000
27 Asia 1962 665770000
28 Asia 1967 754550000
29 Asia 1972 862030000
30 Asia 1977 943455000
31 Asia 1982 1000281000
32 Asia 1987 1084035000
33 Asia 1992 1164970000
34 Asia 1997 1230075000
35 Asia 2002 1280400000
36 Asia 2007 1318683096
37 Europe 1952 69145952
38 Europe 1957 71019069
39 Europe 1962 73739117
40 Europe 1967 76368453
41 Europe 1972 78717088
42 Europe 1977 78160773
43 Europe 1982 78335266
44 Europe 1987 77718298
45 Europe 1992 80597764
46 Europe 1997 82011073
47 Europe 2002 82350671
48 Europe 2007 82400996
49 Oceania 1952 8691212
50 Oceania 1957 9712569
51 Oceania 1962 10794968
52 Oceania 1967 11872264
53 Oceania 1972 13177000
54 Oceania 1977 14074100
55 Oceania 1982 15184200
56 Oceania 1987 16257249
57 Oceania 1992 17481977
58 Oceania 1997 18565243
59 Oceania 2002 19546792
60 Oceania 2007 20434176